Intelligent recognition of bitmapped binary images of text for the purpose of estimating their corresponding character values is often referred to as optical character recognition (“OCR”). Most OCR systems in use today utilize stochastic processes to recognize the text in the graphic images. Because stochastic processes are fundamentally based on chance or probability, these systems are not always as reliable as may be desired. Moreover, the processing time of such stochastic processes can be quite high in some instances and thus not particularly practical.
One attempt to overcome some of the above-noted deficiencies is described in U.S. Pat. No. 5,321,773. The image recognition technique disclosed in the '773 patent is a grammar-based image modeling and recognition system that automatically produces an image decoder based on a finite state network. Although the system described in the '773 patent is substantially faster than the traditional stochastic processes, it is based on stochastic methods (like the traditional approaches) and thus inherently involves chance or probability. Another noteworthy disadvantage of the recognition system in the '773 patent is that it requires extremely detailed font metrics information for the characters to be recognized, including character sidebearings and baseline depths which typically cannot readily obtained. Yet another disadvantage of the image recognition system disclosed in the '773 is that it cannot recognize text when pairs of characters (which may be denoted by black pixels on a white background) have black pixels that overlap.
Since the first patent issued two other related technologies directly dealing with machine generated character glyphs were discovered:
The first is a commercial product named Kleptomania from a company named Structu Rise. According to the Pavel Senatorov CEO, at Structu Rise from its inception their product Kleptomania is not based on Deterministic Finite Automaton (DFA) technology, and its steps are not based on the consecutive parts of a character.
After testing Kleptomania it was clear that the version of Kleptomania downloaded 2007 Apr. 30 had substantially lower accuracy than that of the working prototype of the preferred embodiment of this invention. It was also apparent that the technology of Kleptomania was unable to process character glyphs that had been subject to ClearType® font edge smoothing with any accuracy at all. From this primary research it was determined that Kleptomania is fundamentally different technology with substantially different capabilities.
The second is a paper entitled: Fast Optical Character Recognition through Glyph Hashing for Document Conversion, by Kumar Chellapilla, Patrice Simard, and Radoslav Nickolov all from Microsoft Research. Eighth International Conference on Document Analysis and Recognition (ICDAR '05) pp. 829-834
This technology is also quite different than the technology of the present invention. The only similarity is that the method of this paper also directly deals with machine generated character glyphs. This method is entirely incapable of recognizing character glyphs from graphic images, and is not based on DFA technology.
Every other system that has been encountered for recognizing character glyphs was fundamentally based on a stochastic process, and incapable of recognizing character glyphs at typical 96 dots per inch (DPI), computer display screen resolutions. Market leader OmniPage® 15 was tested and utterly failed to recognize any characters on the test sample submitted to their presales technical support.
In view of the above-noted deficiencies, it would be desirable to provide an image recognition system that is capable of recognizing machine generated text in graphic images with (at least in most cases) complete accuracy. It would further be desirable to provide an image recognition system that is substantially faster than traditional OCR technology, but is also able to recognize text having characters with overlapping black (i.e., foreground) pixels. It would also be desirable to provide an image recognition system that is capable of recognizing machine generated text in graphic images using font metrics information that is readily obtainable.